Frame-by-frame video labeling & content moderation

Project kickoff:

• Stakeholder workshops to define objectives, guardrails, and decision criteria (quality, cost/throughput, auditability)
• Current-state mapping of ingest, labeling, moderation, and reporting flows; identify failure points and cost drivers
• Data governance review (access, retention, PII handling, encryption) and non-functional requirements (SLA, observability)
• Success metrics agreed (e.g., cost per processed minute/frame, precision/recall baseline, latency SLOs)

Outcome: Validated problem statement, target KPIs, and a prioritized scope for the PoC and MVP

• Stand up core, modular pipeline: ingest → deterministic pre-filters (blur/duplication/luminance) → scene boundary detection → analysis interface → storage → reporting slice
• Implement frame selectivity and routing logic to process only signal-rich segments; attach metadata for explainability (frame IDs, thresholds, rationale)
• Build initial dashboards for cost and SLO telemetry; run first end-to-end tests on a representative sample set
• Calibrate thresholds using labeled ground truth; incorporate a lightweight reviewer feedback loop to reduce false positives/negatives

Outcome: Production-grade PoC demonstrating measurable accuracy gains and reduced processing cost on sample content

• Harden orchestration (queues/steps, back-pressure, retries) for stable throughput under peak loads
• Extend reporting for business users (brand safety, placement suitability) and add audit trails for partner transparency
• Security & compliance checks (IAM least-privilege, logging, retention); finalize runbooks and on-call SOPs
• Knowledge transfer with engineering and ops; define post-launch KPI cadence and backlog (new policy packs/classifiers, cost optimizations)

Outcome: Deployed MVP with monitored performance, operational handover completed, and a roadmap for iterative improvements